Optical coherence tomography angiography (OCTA) provides non-invasive visualization of retinal microvasculature, but learning robust representations remains challenging due to sparse vessel structures and strong topological constraints. Many existing self-supervised learning approaches, including masked autoencoders, are primarily designed for dense natural images and rely on uniform masking and pixel-level reconstruction, which may inadequately capture vascular geometry. We propose VAMAE, a vessel-aware masked autoencoding framework for self-supervised pretraining on OCTA images. The approach incorporates anatomically informed masking that emphasizes vessel-rich regions using vesselness and skeleton-based cues, encouraging the model to focus on vascular connectivity and branching patterns. In addition, the pretraining objective includes reconstructing multiple complementary targets, enabling the model to capture appearance, structural, and topological information. We evaluate the proposed pretraining strategy on the OCTA-500 benchmark for several vessel segmentation tasks under varying levels of supervision. The results indicate that vessel-aware masking and multi-target reconstruction provide consistent improvements over standard masked autoencoding baselines, particularly in limited-label settings, suggesting the potential of geometry-aware self-supervised learning for OCTA analysis.
Figures & tables
Figure 1: Vessel-aware masking strategy. Input images are processed to extract vesselness maps, binary vessel maps, and vessel skeletons. Patch-wise structural priors (vessel density and skeletal presence) guide adaptive masking, prioritizing vessel-rich regions for self-supervised pre-training.
Figure 2: VAMAE architecture with vessel-aware masking and multi-target reconstruction of intensity, vesselness, and skeleton.
Method
Type
50% Labels
100% Labels
p -value †
Cohen’s d
Random MAE [ 6 ]
General SSL
68.5 ± 1.3
76.8 ± 0.9
baseline
–
MedMAE [ 13 ]
Medical SSL
69.2 ± 1.1
77.2 ± 0.8
0.113
0.05
BioVessel-Net [ 4 ]
OCTA SSL
72.1 ± 1.0
78.9 ± 0.7
0.002*
0.29
Pissas et al. [ 14 ]
Retinal SSL
73.8 ± 0.9
79.7 ± 0.6
0.001*
0.38
VAMAE (ours)
OCTA SSL
78.4 ± 0.8
82.4 ± 0.5
0.002*
0.71
† Paired t-test vs. Random MAE at 100% labels. *Significant after Bonferroni correction.
Table 1: Comparison with SSL methods on large vessel segmentation (Dice %).
Figure 3: Qualitative segmentation results comparing VAMAE with MAE, and other baselines. VAMAE better preserves thin vessels and complex bifurcations.
Method
50% Labels
100% Labels
Params
Training
U-Net (from scratch) [ 22 ]
56.8 ± 1.4
69.2 ± 0.9
31M
Supervised
CS 2 -Net [ 23 ]
74.3 ± 1.1
81.7 ± 0.6
45M
Supervised
VAMAE (ours)
78.4 ± 0.8
82.4 ± 0.5
86M
SSL + FT
Table 2: Comparison with supervised methods (Dice %).
Task
50% Labels
75% Labels
100% Labels
p -value †
Large Vessel
78.4 ± 0.8 (+9.9)
80.6 ± 0.6 (+6.3)
82.4 ± 0.5 (+5.6)
<0.001*
FAZ
92.7 ± 0.5 (+4.2)
93.6 ± 0.4 (+3.1)
94.1 ± 0.3 (+2.8)
<0.001*
Vein
61.2 ± 0.9 (+6.1)
64.8 ± 0.7 (+5.7)
67.3 ± 0.6 (+5.1)
<0.001*
† Paired t-test vs. Random MAE at 100% labels.
Table 3: Cross-task generalization on OCTA-500 (Dice %). Gains over Random MAE in parentheses.
Configuration
Dice
Δ vs. Baseline
p -value
Random masking (baseline)
76.8 ± 0.9
0.0
–
+ Density masking
79.2 ± 0.7
+2.4
0.002*
+ Hybrid masking ( α=0.6 )
81.6 ± 0.6
+4.8
<0.001*
Hybrid + Multi-target (I+V+S)
82.4 ± 0.5
+5.6
<0.001*
- Skeleton target
80.7 ± 0.6
+3.9
<0.001*
- Vesselness target
79.2 ± 0.8
+2.4
0.004*
Table 4: Component ablation on large vessels at 100% labels (Dice %).
α
Interpretation
Dice
Δ vs. Random
0.0
Pure random (baseline)
76.8 ± 0.9
0.0
0.4
Moderate vessel bias
80.3 ± 0.7
+3.5*
0.6
Balanced (ours)
82.4 ± 0.5
+5.6*
0.8
Strong vessel bias
81.7 ± 0.6
+4.9*
1.0
Deterministic vessel-only
80.1 ± 0.7
+3.3*
*Significant at p<0.001 vs. α=0.0 .
Table 5: Effect of vessel prioritization weight α (Dice %).
Category
n
Random MAE
VAMAE
Δ / p -value
Healthy
80
79.2 ± 0.8
84.4 ± 0.6
+5.2 / <0.001*
Mild DR
48
76.8 ± 1.0
81.6 ± 0.8
+4.8 / <0.001*
Moderate DR
42
74.1 ± 1.3
79.2 ± 1.0
+5.1 / <0.001*
Severe DR
30
68.7 ± 1.9
71.8 ± 1.6
+3.1 / 0.008*
Table 6: Performance by disease severity (Dice %).
Tsinghua Shenzhen International Graduate School, Shenzhen 518109, China · Tsinghua University, Beijing, China · Medical Artificial Intelligence Lab, The First Hospital of Hebei Medical University, Hebei Medical University, Shijiazhuang 050000, China +2
Casey Eye Institute, Oregon Health & Science University, Portland, OR 97239, USA · Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA